Bulletin of the Korean Chemical Society

Korean Chemical Society (대한화학회)
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Molecular Dynamics Free Energy Simulation Study to Rationalize the Relative Activities of PPAR δ Agonists

  • Lee, Woo-Jin (Department of Chemistry, Seoul National University) ;
  • Park, Hwang-Seo (Department of Bioscience and Biotechnology, Sejong University, Seoul 143-747, Korea) ;
  • Lee, Sangyoub (Department of Chemistry, Seoul National University)
  • Published : 2008.02.20
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Abstract

As a computational method for the discovery of the effective agonists for PPARd, we address the usefulness of molecular dynamics free energy (MDFE) simulation with explicit solvent in terms of the accuracy and the computing cost. For this purpose, we establish an efficient computational protocol of thermodynamic integration (TI) that is superior to free energy perturbation (FEP) method in parallel computing environment. Using this protocol, the relative binding affinities of GW501516 and its derivatives for PPARd are calculated. The accuracy of our protocol was evaluated in two steps. First, we devise a thermodynamic cycle to calculate the absolute and relative hydration free energies of test molecules. This allows a self-consistent check for the accuracy of the calculation protocol. Second, the calculated relative binding affinities of the selected ligands are compared with experimental IC50 values. The average deviation of the calculated binding free energies from the experimental results amounts at the most to 1 kcal/mol. The computational efficiency of current protocol is also assessed by comparing its execution times with those of the sequential version of the TI protocol. The results show that the calculation can be accelerated by 4 times when compared to the sequential run. Based on the calculations with the parallel computational protocol, a new potential agonist of GW501516 derivative is proposed.

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References

  1. Willson, T. M.; Wahli, W. Curr. Opin. Chem. Biol. 1997, 1, 235 https://doi.org/10.1016/S1367-5931(97)80015-4
  2. Braissant, O.; Foufelle, F.; Scotto, C.; Dauca, M.; Wahli, W. Endocrinology 1996, 137, 354 https://doi.org/10.1210/en.137.1.354
  3. Sznaidman, M. L.; Haffner, C. D.; Maloney, P. R.; Fivush, A.; Chao, E.; Goreham, D.; Sierra, M. L.; LeGrumelec, C.; Xu, H. E.; Montana, V. G.; Lambert, M. H.; Willson, T. M.; Oliver, W. R., Jr.; Sternbach, D. D. Bioorg. Med. Chem. Lett. 2003, 13, 1517 https://doi.org/10.1016/S0960-894X(03)00207-5
  4. Oliver, W. R.; Shenk, J. L.; Snaith, M. R.; Russell, C. S.; Plunket, K. D.; Bodkin, N. L.; Lewis, M. C.; Winegar, D. A.; Sznaidman, M. L.; Lambert, M. H.; Xu, H. E.; Sternbach, D. D.; Kliewer, S. A.; Hansen, B. C.; Willson, T. M. PNAS 2001, 98, 5306
  5. Wilson, T. M.; Brown, P. J.; Sternbach, D. D.; Henke, B. R. J. Med. Chem. 2000, 43, 527 https://doi.org/10.1021/jm990554g
  6. Wang, Y. X.; Lee, C. H.; Tiep, S.; Yu, R. T.; Ham, J.; Kang, H.; Evans, R. M. Cell 2003 113, 159 https://doi.org/10.1016/S0092-8674(03)00269-1
  7. Kang, H. et al. Gestational treatment with a novel PPAR$\delta$ agonist promotes running endurance (in preparation)
  8. Hamilton, J. A. Prog. Lipid Res. 2004, 43, 177 https://doi.org/10.1016/j.plipres.2003.09.002
  9. Miyamoto, S.; Kollman, P. Proteins 1993, 16, 226 https://doi.org/10.1002/prot.340160303
  10. Oostenbrink, B. C.; Pitera, J. W.; van Lipzig, M. M. H.; Meerman, J. H. N.; van Gunsteren, W. F. J. Med. Chem. 2000, 43, 4594 https://doi.org/10.1021/jm001045d
  11. Steinbrecher, T.; Case, D. A.; Labahn, A. J. Med. Chem. 2006, 49, 1837 https://doi.org/10.1021/jm0505720
  12. Xu, H. E.; Lambert, M. H.; Montana, V. G.; Parks, D. J.; Blanchard, S. G.; Brown, P. J.; Sternbach, D. D.; Lehmann, J. M.; Wisely, G. B.; Willson, T. M.; Kliewer, S. A.; Milburn, M. V. Mol. Cell. 1999, 3, 397 https://doi.org/10.1016/S1097-2765(00)80467-0
  13. Berstein, F. C.; Koetzle, T. F.; Williams, G. J. B.; Meyer Jr., E. F.; Brice, M. D.; Rodgers, J. R.; Kennard, O.; Shimanouchi, T.; Tasumi, M. J. Mol. Biol. 1977, 112, 535 https://doi.org/10.1016/S0022-2836(77)80200-3
  14. Case, D. A.; Darden, T. A.; Cheatham, T. E., III; Simmerling, C. L.; Wang, J.; Duke, R. E.; Luo, R. E.; Merz, K. M.; Wang, B.; Pearlman, D. A.; Crowley, M.; Brozell, S.; Tsui, V.; Gohlke, H.; Mongan, J.; Hornak, V.; Cui, G.; Beroza, P.; Schafmeister, C.; Caldwell, J. W.; Ross, W. S.; Kollman, P. A. AMBER 8; University of California: San Francisco, 2004
  15. Wang, J.; Cieplak, P.; Kollman, P. A. J. Comput. Chem. 2000, 21, 1049 https://doi.org/10.1002/1096-987X(200009)21:12<1049::AID-JCC3>3.0.CO;2-F
  16. Mehler, E. L.; Solmajer, T. Protein. Eng. 1991, 4, 903 https://doi.org/10.1093/protein/4.8.903
  17. Halgren, T. A. J. Comput. Chem. 1999, 20, 720 https://doi.org/10.1002/(SICI)1096-987X(199905)20:7<720::AID-JCC7>3.0.CO;2-X
  18. Gamess 2004. Nov.
  19. Bayly, C. I.; Cieplak, P.; Cornell, W. D.; Kollman, P. A. J. Phys. Chem. 1993, 97, 10269 https://doi.org/10.1021/j100142a004
  20. Stouten, P. F. W.; Frommel, C.; Nakamura, H.; Sander, C. Mol. Simul. 1993, 10, 97 https://doi.org/10.1080/08927029308022161
  21. Wang, J.; Wolf, R. M.; Caldwell, J. W.; Kollman, P. A.; Case, D. A. J. Comput. Chem. 2004, 25, 1157 https://doi.org/10.1002/jcc.20035
  22. Jorgensen, W. L.; Chandrasekhar, J.; Madura, J. D.; Impey, R. W.; Klein, M. L. J. Chem. Phys. 1983, 79, 926 https://doi.org/10.1063/1.445869
  23. Morris, G. M.; Goodsell, D. S.; Halliday, R. S.; Huey, R.; Hart, W. E.; Belew, R. K.; Olson, A. J. J. Comput. Chem. 1998, 19, 1639 https://doi.org/10.1002/(SICI)1096-987X(19981115)19:14<1639::AID-JCC10>3.0.CO;2-B
  24. Ryckaert, J. P.; Ciccotti, G.; Berendsen, H. J. C. J. Comput. Phys. 1977, 23, 327 https://doi.org/10.1016/0021-9991(77)90098-5
  25. Darden, D.; York, D.; Pedersen, L. J. Chem. Phys. 1993, 98, 10089 https://doi.org/10.1063/1.464397
  26. Berendsen, H. J. C.; Postma, J. P. M.; van Gunsteren, W. F.; DiNola, A.; Haak, J. R. J. Chem. Phys. 1984, 81, 3684 https://doi.org/10.1063/1.448118
  27. Tembe, B. L.; McCammon, J. A. Comput. Chem. 1984, 8, 281 https://doi.org/10.1016/0097-8485(84)85020-2
  28. Wong, C. F.; McCammon, J. A. J. Am. Chem. Soc. 1986, 108, 3830 https://doi.org/10.1021/ja00273a048
  29. Beveridge, D. L.; DiCapua, F. M. Annu. Rev. Biophys. Biophys. Chem. 1989, 18, 431 https://doi.org/10.1146/annurev.bb.18.060189.002243
  30. Simonson, T. Mol. Phys. 1993, 80, 441 https://doi.org/10.1080/00268979300102371
  31. Bash, P. A.; Singh, U. C.; Langridge, R.; Kollman, P. A. Science 1987, 236, 564 https://doi.org/10.1126/science.3576184
  32. Nonlinear mixing rule used in amber8, http://amber.scripps.edu/doc8/amber8.pdf
  33. Encyclopedia of Computational Chemistry; Vol. 2 pp 1036-1061
  34. Amber tutorial for TI calculation http://amber.scripps.edu/tutorial/shirts/index.html
  35. Murray, C. W.; Baxter, C. A.; Frenkel, D. A. J. Comput.-Aided Mol. Des. 1999, 13, 547 https://doi.org/10.1023/A:1008015827877
  36. Shirts, M. R.; Pietera, J. W.; Swope, W. C.; Pande, V. S. J. Chem. Phys. 2003, 119, 5740 https://doi.org/10.1063/1.1587119

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